# SplitApplyCombine.jl \`group\` enhancements, reaches version 1.0.0 **URL:** \ **Category:** Data\ **Tags:** announcement, splitapplycombine\ **Created:** [December 25, 2019, 2:47am UTC](https://discourse.julialang.org/t/splitapplycombine-jl-group-enhancements-reaches-version-1-0-0/32675 "2019-12-25T02:47:13Z")\ **Posts on this page:** 2\ **Page:** 1

**Author:** ![andyferris](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/andyferris/32/235_2.png) [@andyferris](https://discourse.julialang.org/u/andyferris)\ **Post date:** [December 25, 2019, 2:47am UTC](https://discourse.julialang.org/t/splitapplycombine-jl-group-enhancements-reaches-version-1-0-0/32675/1 "2019-12-25T02:47:13Z")

Merry Chirstmas everybody, Today I have released version 1.0.0 of [SplitApplyCombine.jl](https://github.com/JuliaData/SplitApplyCombine.jl), with the headline feature being that grouping functions have moved to returning a dictionary from [Dictionaries.jl](https://github.com/andyferris/Dictionaries.jl). On a personal note, this is a big deal for me. Let me explain. While I found I used the previous `group` function from _SplitApplyCombine_ quite frequently to analyse and explore data, once I had the groups it was difficult do perform further analysis. ```julia julia> using SplitApplyCombine, Statistics julia> group(iseven, 1:10) Dict{Bool,Array{Int64,1}} with 2 entries: false => [1, 3, 5, 7, 9] true => [2, 4, 6, 8, 10] julia> mean.(group(iseven, 1:10)) ERROR: ArgumentError: broadcasting over dictionaries and `NamedTuple`s is reserved Stacktrace: [1] broadcastable(::Dict{Bool,Array{Int64,1}}) at ./broadcast.jl:618 [2] broadcasted(::Function, ::Dict{Bool,Array{Int64,1}}) at ./broadcast.jl:1166 [3] top-level scope at none:0 ``` While one can construct a new `Dict` with what I want, it was inconvenient enough to add a bunch of convenience functions like `groupsum` and `groupcount` (well, there are performance reasons for this, too). In fact, `groupcount` may be one of my most used functions when exploring data: ```julia julia> groupcount(iseven, 1:10) Dict{Bool,Int64} with 2 entries: false => 5 true => 5 ``` In any case, the difficulties were enough to spawn my interest in the dictionary interface and ultimately lead to [Dictionaries.jl](https://github.com/andyferris/Dictionaries.jl), which was a bunch of work. But at the end of the day it was motivated by my desire to find e.g. the `mean` of `group`s, so without further ado, this is what you get with _SplitApplyCombine_ 1.0.0: ```julia julia> group(iseven, 1:10) 2-element Dictionaries.HashDictionary{Bool,Array{Int64,1}} false │ [1, 3, 5, 7, 9] true │ [2, 4, 6, 8, 10] julia> mean.(group(iseven, 1:10)) 2-element Dictionaries.HashDictionary{Bool,Float64} false │ 5.0 true │ 6.0 ``` Hurray! Merry Christmas, me! 🙂 I also addressed another usability concern - while sometimes determining the grouping keys via a function `group(by::Function, a)` you may already know the groups. A common case is grouping data by a column of a dataframe, so let’s look at that. In the spirit of the day, I found a CSV file on the internet called [Christmas.csv](https://github.com/ali-ce/datasets/blob/master/VIntage-Ads/Christmas.csv). It’s about Christmas-themed advertisements, and here is a sample: ```julia shell> head -5 Christmas.csv Name,Brand,Decade,Image.src,Find more here 7up (1948),7up,1940,http://file.vintageadbrowser.com/0clbm89x7h7efw.jpg,http://www.vintageadbrowser.com/xmas-ads-1940s/14 7-Up Soda Bottle Santa Hand Christmas (1964),7up,1960,http://file.vintageadbrowser.com/lzfxsdpk7tz7kp.jpg,http://www.vintageadbrowser.com/xmas-ads-1960s "A.H. Grebe & Company’s Radio – It is our sincere hope that the gifts you make this Christmas may bring to the little worlds into which they go, something of the joy and happiness t (1927)",A.H. Grebe & Company’s Radio,1920,http://file.vintageadbrowser.com/rczr6m2331lbn6.jpg,http://www.vintageadbrowser.com/xmas-ads-1920s/2 Christmas Tree Art A&p Coffee (1958),A&P,1950,http://file.vintageadbrowser.com/hf83k5lw0icn9h.jpg,http://www.vintageadbrowser.com/xmas-ads-1950s/9 ``` We can read it into the REPL - it’s a bit wide to see all the columns of all the rows, but here is the first row. ```julia julia> using SplitApplyCombine, CSV, Statistics julia> df = CSV.read("Christmas.csv"); df[1, :] DataFrameRow │ Row │ Name │ Brand │ Decade │ Image.src │ Find more here │ │ │ String │ String │ Int64 │ String │ String │ ├─────┼────────────┼────────┼────────┼─────────────────────────────────────────────────────┼───────────────────────────────────────────────────┤ │ 1 │ 7up (1948) │ 7up │ 1940 │ http://file.vintageadbrowser.com/0clbm89x7h7efw.jpg │ http://www.vintageadbrowser.com/xmas-ads-1940s/14 │ ``` The first thing I would do with a dataset like this is try to understand some basic distributions, like how often does a brand appear in the list? ```julia julia> counts = groupcount(df.Brand) 279-element Dictionaries.HashDictionary{String,Int64} "Lejon" │ 1 "Ford Motor Company" │ 1 "Texaco" │ 1 "Hoover" │ 1 "Philip's" │ 1 "PG&E" │ 1 "Nunnaly" │ 1 "Wilcox-Gay" │ 1 "Alcoa Aluminum" │ 1 "DuMont" │ 1 "General Electric" │ 7 "American Greeting" │ 1 "Barbasol" │ 1 "Hardware Mutual" │ 1 ⋮ │ ⋮ julia> mean(counts) 2.010752688172043 julia> findmax(counts) (42, "Kodak") ``` I guess Kodak figured out that people like to take photographs at Christmas time? 🙂 Note that while `findmax` works fine on `Dict`, the `mean` requires `mean(values(counts))`. A minor detail to be sure, but every bit of convenience helps. The newer functionality comes in the form of `group(groups, values)`, where the first is a collection of the same size as the second, which seems particularly useful with tabular data. ```julia julia> group(df.Brand, df.Decade) 279-element Dictionaries.HashDictionary{String,Array{Int64,1}} "Lejon" │ [1940] "Ford Motor Company" │ [1950] "Texaco" │ [1960] "Hoover" │ [1960] "Philip's" │ [1940] "PG&E" │ [1960] "Nunnaly" │ [1920] "Wilcox-Gay" │ [1940] "Alcoa Aluminum" │ [1960] "DuMont" │ [1940] "General Electric" │ [1940, 1940, 1940, 1940, 1940, 1940, 1960] "American Greeting" │ [1940] "Barbasol" │ [1940] "Hardware Mutual" │ [1950] ⋮ │ ⋮ ``` We also have new functions `groupunique` and `grouponly`, which may be somewhat useful in some situations. For example: ```julia julia> groupunique(df.Decade, df.Brand) 10-element Dictionaries.HashDictionary{Int64,Dictionaries.HashIndices{String}} 1980 │ {"Alexander O’Neal", "Absolut Vodka", "Nynex", "Seagram's", "Cutty Sark", "Baileys", "A… 1990 │ {"Barbie", "Absolut Vodka", "Bucks", "BlueBlocker", "Hewlett Packard", "Jack Daniel’s",… 1930 │ {"Camel", "Four Roses", "The Etude Music Magazine", "Elgin Watch", "Underwood", "Hamilt… 1970 │ {"Max Factor", "Chivas Regal", "Jerry Silverman", "Northwest Christmas Tree Association… 1900 │ {"Kodak", "Wanamaker", "H. O’Neill & Co.", "Other", "Gates Potteries", "Citizens Nation… 1920 │ {"Johnston’s", "Willys-Overland Six", "Camel", "Atwater", "A.H. Grebe & Company’s Radio… 1960 │ {"Tiffany", "Max Factor", "AMF", "Guerlain", "Revlon", "Texaco", "Four Roses", "Hoover"… 1940 │ {"Lejon", "Sportsman", "Sunbeam", "Philip's", "Arrow", "Jantzen", "Pennsylvania Railroa… 1910 │ {"Kodak", "Other", "Blue Bird", "Larkin Factory", "FDT Florist"} 1950 │ {"Ford Motor Company", "Air Express", "Jell-o", "Arrow", "New York Central Railroad", "… julia> length.(ans) 10-element Dictionaries.HashDictionary{Int64,Int64} 1980 │ 15 1990 │ 9 1930 │ 26 1970 │ 19 1900 │ 6 1920 │ 21 1960 │ 35 1940 │ 137 1910 │ 5 1950 │ 91 ``` Anyway, that’s it for now. I feel the future holds some interesting work around tables which behave like dictionaries, contain primary keys or are partitioned, and grouping functions that return flattened containers in a similar vein to SQL and `DataFrames.groupby`. Happy holidays! Andy ---

**Author:** ![yurivish](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/yurivish/32/307_2.png) [@yurivish](https://discourse.julialang.org/u/yurivish)\ **Post date:** [December 25, 2019, 3:13am UTC](https://discourse.julialang.org/t/splitapplycombine-jl-group-enhancements-reaches-version-1-0-0/32675/2 "2019-12-25T03:13:07Z")

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